{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-29T08:58:49.686513Z",
     "start_time": "2019-05-29T08:58:49.331424Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.arange(5)\n",
    "y1 = [0.928, 0.97, 0.968, 0.974, 0.98]\n",
    "y2 = [\n",
    "    0.89, 0.93, 0.959686450167973, 0.979843225083986,\n",
    "    0.975363941769317\n",
    "]\n",
    "\n",
    "plt.xlim(1, 5)\n",
    "plt.ylim(0.90, 1.0)\n",
    "\n",
    "plt.xticks([1, 2, 3, 4, 5])\n",
    "\n",
    "plt.plot(x, y1, label='Development accuracy', color='b', marker='^')\n",
    "plt.plot(x, y2, label='Test accuracy', color='r', marker='X')\n",
    "\n",
    "plt.xlabel('Epoches')\n",
    "plt.ylabel('Accuracy')\n",
    "\n",
    "plt.legend(loc=4)\n",
    "\n",
    "plt.savefig('accuracy.jpg', dpi=300)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
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    "lenType": 16,
    "lenVar": 40
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
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     "varRefreshCmd": "print(var_dic_list())"
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     "delete_cmd_postfix": ") ",
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   "types_to_exclude": [
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 "nbformat": 4,
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